Overview

Dataset statistics

Number of variables22
Number of observations8632
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 MiB
Average record size in memory214.6 B

Variable types

Numeric21
Categorical1

Alerts

balance is highly overall correlated with balance_freq and 5 other fieldsHigh correlation
balance_freq is highly overall correlated with balance and 1 other fieldsHigh correlation
purchases is highly overall correlated with one_purchases and 7 other fieldsHigh correlation
one_purchases is highly overall correlated with purchases and 3 other fieldsHigh correlation
install_purchases is highly overall correlated with purchases and 4 other fieldsHigh correlation
cash_adv is highly overall correlated with balance and 3 other fieldsHigh correlation
purchases_freq is highly overall correlated with purchases and 5 other fieldsHigh correlation
one_purchases_freq is highly overall correlated with purchases and 4 other fieldsHigh correlation
purchases_install_freq is highly overall correlated with purchases and 4 other fieldsHigh correlation
cash_adv_freq is highly overall correlated with balance and 3 other fieldsHigh correlation
cash_adv_trx is highly overall correlated with balance and 3 other fieldsHigh correlation
purchases_trx is highly overall correlated with purchases and 7 other fieldsHigh correlation
min_pay is highly overall correlated with balance and 1 other fieldsHigh correlation
prc_full_pay is highly overall correlated with balanceHigh correlation
avg_ticket_expenses is highly overall correlated with purchases and 6 other fieldsHigh correlation
credit_limit_rate is highly overall correlated with purchases and 7 other fieldsHigh correlation
one_payment is highly overall correlated with one_purchases_freqHigh correlation
debit_rate is highly skewed (γ1 = 35.88491655)Skewed
id is uniformly distributedUniform
id has unique valuesUnique
payments has unique valuesUnique
debit_rate has unique valuesUnique
purchases has 1966 (22.8%) zerosZeros
one_purchases has 4110 (47.6%) zerosZeros
install_purchases has 3745 (43.4%) zerosZeros
cash_adv has 4427 (51.3%) zerosZeros
purchases_freq has 1966 (22.8%) zerosZeros
one_purchases_freq has 4110 (47.6%) zerosZeros
purchases_install_freq has 3745 (43.4%) zerosZeros
cash_adv_freq has 4427 (51.3%) zerosZeros
cash_adv_trx has 4427 (51.3%) zerosZeros
purchases_trx has 1963 (22.7%) zerosZeros
prc_full_pay has 5587 (64.7%) zerosZeros
credit_limit_rate has 1966 (22.8%) zerosZeros

Reproduction

Analysis started2023-02-07 15:54:53.780066
Analysis finished2023-02-07 15:56:04.235673
Duration1 minute and 10.46 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct8632
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14603.271
Minimum10001
Maximum19190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-02-07T07:56:04.377912image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10001
5-th percentile10487.55
Q112338.75
median14593.5
Q316886.25
95-th percentile18719.45
Maximum19190
Range9189
Interquartile range (IQR)4547.5

Descriptive statistics

Standard deviation2633.0359
Coefficient of variation (CV)0.18030453
Kurtosis-1.1889633
Mean14603.271
Median Absolute Deviation (MAD)2273.5
Skewness0.0019343185
Sum1.2605543 × 108
Variance6932877.9
MonotonicityStrictly increasing
2023-02-07T07:56:04.581676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10001 1
 
< 0.1%
16112 1
 
< 0.1%
16126 1
 
< 0.1%
16125 1
 
< 0.1%
16124 1
 
< 0.1%
16123 1
 
< 0.1%
16122 1
 
< 0.1%
16121 1
 
< 0.1%
16120 1
 
< 0.1%
16119 1
 
< 0.1%
Other values (8622) 8622
99.9%
ValueCountFrequency (%)
10001 1
< 0.1%
10002 1
< 0.1%
10003 1
< 0.1%
10005 1
< 0.1%
10006 1
< 0.1%
10007 1
< 0.1%
10008 1
< 0.1%
10009 1
< 0.1%
10010 1
< 0.1%
10011 1
< 0.1%
ValueCountFrequency (%)
19190 1
< 0.1%
19189 1
< 0.1%
19188 1
< 0.1%
19186 1
< 0.1%
19184 1
< 0.1%
19183 1
< 0.1%
19182 1
< 0.1%
19181 1
< 0.1%
19180 1
< 0.1%
19179 1
< 0.1%

balance
Real number (ℝ)

Distinct8627
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1601.9124
Minimum0
Maximum19043.139
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-02-07T07:56:04.758816image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.297958
Q1148.47736
median917.63234
Q32106.0281
95-th percentile5937.2098
Maximum19043.139
Range19043.139
Interquartile range (IQR)1957.5507

Descriptive statistics

Standard deviation2095.8105
Coefficient of variation (CV)1.3083178
Kurtosis7.5507097
Mean1601.9124
Median Absolute Deviation (MAD)826.17398
Skewness2.3737272
Sum13827708
Variance4392421.6
MonotonicityNot monotonic
2023-02-07T07:56:04.929848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6
 
0.1%
40.900749 1
 
< 0.1%
296.905944 1
 
< 0.1%
765.109593 1
 
< 0.1%
2583.247881 1
 
< 0.1%
1146.669364 1
 
< 0.1%
757.470201 1
 
< 0.1%
1253.188317 1
 
< 0.1%
5058.299635 1
 
< 0.1%
1084.652647 1
 
< 0.1%
Other values (8617) 8617
99.8%
ValueCountFrequency (%)
0 6
0.1%
0.000199 1
 
< 0.1%
0.001146 1
 
< 0.1%
0.001214 1
 
< 0.1%
0.001289 1
 
< 0.1%
0.004816 1
 
< 0.1%
0.009684 1
 
< 0.1%
0.064811 1
 
< 0.1%
0.065402 1
 
< 0.1%
0.074724 1
 
< 0.1%
ValueCountFrequency (%)
19043.13856 1
< 0.1%
18495.55855 1
< 0.1%
16304.88925 1
< 0.1%
16259.44857 1
< 0.1%
16115.5964 1
< 0.1%
15532.33972 1
< 0.1%
15258.2259 1
< 0.1%
15244.74865 1
< 0.1%
15155.53286 1
< 0.1%
14581.45914 1
< 0.1%

balance_freq
Real number (ℝ)

Distinct42
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.89513391
Minimum0
Maximum1
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-02-07T07:56:05.104729image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.363636
Q10.909091
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.090909

Descriptive statistics

Standard deviation0.20757666
Coefficient of variation (CV)0.23189454
Kurtosis3.3795572
Mean0.89513391
Median Absolute Deviation (MAD)0
Skewness-2.0859805
Sum7726.7959
Variance0.043088071
MonotonicityNot monotonic
2023-02-07T07:56:05.284279image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1 6128
71.0%
0.909091 406
 
4.7%
0.818182 274
 
3.2%
0.727273 220
 
2.5%
0.545455 217
 
2.5%
0.636364 202
 
2.3%
0.454545 169
 
2.0%
0.363636 167
 
1.9%
0.272727 140
 
1.6%
0.181818 117
 
1.4%
Other values (32) 592
 
6.9%
ValueCountFrequency (%)
0 6
 
0.1%
0.090909 25
 
0.3%
0.1 2
 
< 0.1%
0.125 2
 
< 0.1%
0.142857 1
 
< 0.1%
0.166667 1
 
< 0.1%
0.181818 117
1.4%
0.2 7
 
0.1%
0.222222 2
 
< 0.1%
0.25 5
 
0.1%
ValueCountFrequency (%)
1 6128
71.0%
0.909091 406
 
4.7%
0.9 55
 
0.6%
0.888889 53
 
0.6%
0.875 57
 
0.7%
0.857143 50
 
0.6%
0.833333 59
 
0.7%
0.818182 274
 
3.2%
0.8 20
 
0.2%
0.777778 21
 
0.2%

purchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6012
Distinct (%)69.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1025.818
Minimum0
Maximum49039.57
Zeros1966
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-02-07T07:56:05.453442image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q143.765
median375.745
Q31146.42
95-th percentile4060.2225
Maximum49039.57
Range49039.57
Interquartile range (IQR)1102.655

Descriptive statistics

Standard deviation2167.5288
Coefficient of variation (CV)2.112976
Kurtosis108.63701
Mean1025.818
Median Absolute Deviation (MAD)375.745
Skewness8.0543316
Sum8854860.8
Variance4698181.1
MonotonicityNot monotonic
2023-02-07T07:56:05.647912image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1966
 
22.8%
45.65 25
 
0.3%
150 15
 
0.2%
60 13
 
0.2%
100 12
 
0.1%
200 12
 
0.1%
450 12
 
0.1%
600 10
 
0.1%
70 10
 
0.1%
50 9
 
0.1%
Other values (6002) 6548
75.9%
ValueCountFrequency (%)
0 1966
22.8%
0.01 3
 
< 0.1%
0.05 1
 
< 0.1%
1 2
 
< 0.1%
2 1
 
< 0.1%
4.44 1
 
< 0.1%
4.8 1
 
< 0.1%
4.99 1
 
< 0.1%
6.9 1
 
< 0.1%
7.26 1
 
< 0.1%
ValueCountFrequency (%)
49039.57 1
< 0.1%
41050.4 1
< 0.1%
40040.71 1
< 0.1%
38902.71 1
< 0.1%
35131.16 1
< 0.1%
32539.78 1
< 0.1%
31299.35 1
< 0.1%
27957.68 1
< 0.1%
27790.42 1
< 0.1%
26784.62 1
< 0.1%

one_purchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3921
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean605.18172
Minimum0
Maximum40761.25
Zeros4110
Zeros (%)47.6%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-02-07T07:56:05.830875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median45
Q3599.6625
95-th percentile2729.1745
Maximum40761.25
Range40761.25
Interquartile range (IQR)599.6625

Descriptive statistics

Standard deviation1684.6477
Coefficient of variation (CV)2.7837056
Kurtosis160.05867
Mean605.18172
Median Absolute Deviation (MAD)45
Skewness9.9338882
Sum5223928.6
Variance2838037.9
MonotonicityNot monotonic
2023-02-07T07:56:06.034742image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4110
47.6%
45.65 43
 
0.5%
50 16
 
0.2%
200 15
 
0.2%
100 12
 
0.1%
150 12
 
0.1%
1000 12
 
0.1%
70 12
 
0.1%
250 11
 
0.1%
60 10
 
0.1%
Other values (3911) 4379
50.7%
ValueCountFrequency (%)
0 4110
47.6%
0.01 6
 
0.1%
0.02 2
 
< 0.1%
0.05 1
 
< 0.1%
1 4
 
< 0.1%
1.4 1
 
< 0.1%
2 1
 
< 0.1%
4.99 1
 
< 0.1%
5 1
 
< 0.1%
6.9 1
 
< 0.1%
ValueCountFrequency (%)
40761.25 1
< 0.1%
40624.06 1
< 0.1%
34087.73 1
< 0.1%
33803.84 1
< 0.1%
26547.43 1
< 0.1%
26514.32 1
< 0.1%
25122.77 1
< 0.1%
24543.52 1
< 0.1%
23032.97 1
< 0.1%
22257.39 1
< 0.1%

install_purchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4339
Distinct (%)50.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean420.94495
Minimum0
Maximum22500
Zeros3745
Zeros (%)43.4%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-02-07T07:56:06.249539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median94.785
Q3484.1475
95-th percentile1800
Maximum22500
Range22500
Interquartile range (IQR)484.1475

Descriptive statistics

Standard deviation917.42729
Coefficient of variation (CV)2.1794472
Kurtosis94.158059
Mean420.94495
Median Absolute Deviation (MAD)94.785
Skewness7.2148716
Sum3633596.8
Variance841672.84
MonotonicityNot monotonic
2023-02-07T07:56:06.420831image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3745
43.4%
100 14
 
0.2%
200 13
 
0.2%
150 11
 
0.1%
125 11
 
0.1%
300 10
 
0.1%
75 9
 
0.1%
500 8
 
0.1%
450 8
 
0.1%
360 7
 
0.1%
Other values (4329) 4796
55.6%
ValueCountFrequency (%)
0 3745
43.4%
1.95 1
 
< 0.1%
4.44 1
 
< 0.1%
4.8 1
 
< 0.1%
6.33 1
 
< 0.1%
7.26 1
 
< 0.1%
7.67 1
 
< 0.1%
9.58 1
 
< 0.1%
9.65 1
 
< 0.1%
9.68 1
 
< 0.1%
ValueCountFrequency (%)
22500 1
< 0.1%
15497.19 1
< 0.1%
14686.1 1
< 0.1%
13184.43 1
< 0.1%
12738.47 1
< 0.1%
12560.85 1
< 0.1%
12541 1
< 0.1%
12375 1
< 0.1%
12235.05 1
< 0.1%
12128.94 1
< 0.1%

cash_adv
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4206
Distinct (%)48.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean994.63622
Minimum0
Maximum47137.212
Zeros4427
Zeros (%)51.3%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-02-07T07:56:06.595812image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31133.7529
95-th percentile4723.928
Maximum47137.212
Range47137.212
Interquartile range (IQR)1133.7529

Descriptive statistics

Standard deviation2121.8418
Coefficient of variation (CV)2.1332843
Kurtosis52.12473
Mean994.63622
Median Absolute Deviation (MAD)0
Skewness5.1386193
Sum8585699.8
Variance4502212.8
MonotonicityNot monotonic
2023-02-07T07:56:06.771873image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4427
51.3%
2411.584248 1
 
< 0.1%
92.6579 1
 
< 0.1%
1486.243293 1
 
< 0.1%
855.232779 1
 
< 0.1%
3767.104707 1
 
< 0.1%
291.608512 1
 
< 0.1%
38.690552 1
 
< 0.1%
521.664369 1
 
< 0.1%
1974.202963 1
 
< 0.1%
Other values (4196) 4196
48.6%
ValueCountFrequency (%)
0 4427
51.3%
14.222216 1
 
< 0.1%
18.042768 1
 
< 0.1%
18.117967 1
 
< 0.1%
18.123413 1
 
< 0.1%
18.126683 1
 
< 0.1%
18.149946 1
 
< 0.1%
18.204577 1
 
< 0.1%
18.240626 1
 
< 0.1%
18.280043 1
 
< 0.1%
ValueCountFrequency (%)
47137.21176 1
< 0.1%
29282.10915 1
< 0.1%
27296.48576 1
< 0.1%
26268.69989 1
< 0.1%
26194.04954 1
< 0.1%
23130.82106 1
< 0.1%
22665.7785 1
< 0.1%
21943.84942 1
< 0.1%
20712.67008 1
< 0.1%
20277.33112 1
< 0.1%

purchases_freq
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49605607
Minimum0
Maximum1
Zeros1966
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-02-07T07:56:06.941277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.083333
median0.5
Q30.916667
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.833334

Descriptive statistics

Standard deviation0.40127596
Coefficient of variation (CV)0.80893266
Kurtosis-1.6379842
Mean0.49605607
Median Absolute Deviation (MAD)0.416667
Skewness0.032666608
Sum4281.956
Variance0.1610224
MonotonicityNot monotonic
2023-02-07T07:56:07.142611image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1 2125
24.6%
0 1966
22.8%
0.083333 620
 
7.2%
0.916667 391
 
4.5%
0.5 390
 
4.5%
0.833333 367
 
4.3%
0.166667 367
 
4.3%
0.333333 349
 
4.0%
0.25 328
 
3.8%
0.583333 309
 
3.6%
Other values (37) 1420
16.5%
ValueCountFrequency (%)
0 1966
22.8%
0.083333 620
 
7.2%
0.090909 41
 
0.5%
0.1 23
 
0.3%
0.111111 16
 
0.2%
0.125 25
 
0.3%
0.142857 22
 
0.3%
0.166667 367
 
4.3%
0.181818 15
 
0.2%
0.2 17
 
0.2%
ValueCountFrequency (%)
1 2125
24.6%
0.916667 391
 
4.5%
0.909091 28
 
0.3%
0.9 23
 
0.3%
0.888889 18
 
0.2%
0.875 26
 
0.3%
0.857143 23
 
0.3%
0.833333 367
 
4.3%
0.818182 20
 
0.2%
0.8 9
 
0.1%

one_purchases_freq
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2059945
Minimum0
Maximum1
Zeros4110
Zeros (%)47.6%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-02-07T07:56:07.342300image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.083333
Q30.333333
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.333333

Descriptive statistics

Standard deviation0.30009566
Coefficient of variation (CV)1.4568139
Kurtosis1.0560114
Mean0.2059945
Median Absolute Deviation (MAD)0.083333
Skewness1.5035616
Sum1778.1446
Variance0.090057407
MonotonicityNot monotonic
2023-02-07T07:56:07.539134image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 4110
47.6%
0.083333 1056
 
12.2%
0.166667 576
 
6.7%
1 469
 
5.4%
0.25 408
 
4.7%
0.333333 346
 
4.0%
0.416667 243
 
2.8%
0.5 232
 
2.7%
0.583333 197
 
2.3%
0.666667 167
 
1.9%
Other values (37) 828
 
9.6%
ValueCountFrequency (%)
0 4110
47.6%
0.083333 1056
 
12.2%
0.090909 54
 
0.6%
0.1 36
 
0.4%
0.111111 24
 
0.3%
0.125 35
 
0.4%
0.142857 33
 
0.4%
0.166667 576
 
6.7%
0.181818 33
 
0.4%
0.2 26
 
0.3%
ValueCountFrequency (%)
1 469
5.4%
0.916667 151
 
1.7%
0.909091 4
 
< 0.1%
0.9 1
 
< 0.1%
0.888889 2
 
< 0.1%
0.875 6
 
0.1%
0.857143 1
 
< 0.1%
0.833333 115
 
1.3%
0.818182 10
 
0.1%
0.8 4
 
< 0.1%

purchases_install_freq
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.36882714
Minimum0
Maximum1
Zeros3745
Zeros (%)43.4%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-02-07T07:56:07.726807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.166667
Q30.75
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.39809538
Coefficient of variation (CV)1.0793549
Kurtosis-1.4194671
Mean0.36882714
Median Absolute Deviation (MAD)0.166667
Skewness0.48759201
Sum3183.7158
Variance0.15847993
MonotonicityNot monotonic
2023-02-07T07:56:07.917703image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 3745
43.4%
1 1296
 
15.0%
0.416667 381
 
4.4%
0.916667 340
 
3.9%
0.833333 304
 
3.5%
0.5 303
 
3.5%
0.166667 296
 
3.4%
0.666667 290
 
3.4%
0.75 284
 
3.3%
0.083333 248
 
2.9%
Other values (37) 1145
 
13.3%
ValueCountFrequency (%)
0 3745
43.4%
0.083333 248
 
2.9%
0.090909 10
 
0.1%
0.1 5
 
0.1%
0.111111 8
 
0.1%
0.125 4
 
< 0.1%
0.142857 5
 
0.1%
0.166667 296
 
3.4%
0.181818 14
 
0.2%
0.2 8
 
0.1%
ValueCountFrequency (%)
1 1296
15.0%
0.916667 340
 
3.9%
0.909091 25
 
0.3%
0.9 18
 
0.2%
0.888889 28
 
0.3%
0.875 28
 
0.3%
0.857143 29
 
0.3%
0.833333 304
 
3.5%
0.818182 20
 
0.2%
0.8 17
 
0.2%

cash_adv_freq
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct54
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13766797
Minimum0
Maximum1.5
Zeros4427
Zeros (%)51.3%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-02-07T07:56:08.105038image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.25
95-th percentile0.583333
Maximum1.5
Range1.5
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.20181644
Coefficient of variation (CV)1.4659651
Kurtosis3.1816644
Mean0.13766797
Median Absolute Deviation (MAD)0
Skewness1.7952786
Sum1188.3499
Variance0.040729874
MonotonicityNot monotonic
2023-02-07T07:56:08.271094image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4427
51.3%
0.083333 980
 
11.4%
0.166667 730
 
8.5%
0.25 573
 
6.6%
0.333333 434
 
5.0%
0.416667 272
 
3.2%
0.5 209
 
2.4%
0.583333 142
 
1.6%
0.666667 124
 
1.4%
0.090909 66
 
0.8%
Other values (44) 675
 
7.8%
ValueCountFrequency (%)
0 4427
51.3%
0.083333 980
 
11.4%
0.090909 66
 
0.8%
0.1 36
 
0.4%
0.111111 23
 
0.3%
0.125 43
 
0.5%
0.142857 43
 
0.5%
0.166667 730
 
8.5%
0.181818 41
 
0.5%
0.2 21
 
0.2%
ValueCountFrequency (%)
1.5 1
 
< 0.1%
1.25 1
 
< 0.1%
1.166667 2
 
< 0.1%
1.142857 1
 
< 0.1%
1.125 1
 
< 0.1%
1.1 1
 
< 0.1%
1.090909 1
 
< 0.1%
1 24
0.3%
0.916667 27
0.3%
0.909091 3
 
< 0.1%

cash_adv_trx
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3154541
Minimum0
Maximum123
Zeros4427
Zeros (%)51.3%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-02-07T07:56:08.444724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile15
Maximum123
Range123
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.9137395
Coefficient of variation (CV)2.0853069
Kurtosis60.407602
Mean3.3154541
Median Absolute Deviation (MAD)0
Skewness5.6723042
Sum28619
Variance47.799794
MonotonicityNot monotonic
2023-02-07T07:56:08.619869image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4427
51.3%
1 839
 
9.7%
2 602
 
7.0%
3 429
 
5.0%
4 374
 
4.3%
5 300
 
3.5%
6 241
 
2.8%
7 202
 
2.3%
8 169
 
2.0%
10 147
 
1.7%
Other values (55) 902
 
10.4%
ValueCountFrequency (%)
0 4427
51.3%
1 839
 
9.7%
2 602
 
7.0%
3 429
 
5.0%
4 374
 
4.3%
5 300
 
3.5%
6 241
 
2.8%
7 202
 
2.3%
8 169
 
2.0%
9 108
 
1.3%
ValueCountFrequency (%)
123 3
< 0.1%
110 1
 
< 0.1%
107 1
 
< 0.1%
93 1
 
< 0.1%
80 1
 
< 0.1%
71 1
 
< 0.1%
69 1
 
< 0.1%
63 1
 
< 0.1%
62 3
< 0.1%
61 1
 
< 0.1%

purchases_trx
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct173
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.040199
Minimum0
Maximum358
Zeros1963
Zeros (%)22.7%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-02-07T07:56:08.793822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q318
95-th percentile59
Maximum358
Range358
Interquartile range (IQR)17

Descriptive statistics

Standard deviation25.184222
Coefficient of variation (CV)1.6744607
Kurtosis33.942251
Mean15.040199
Median Absolute Deviation (MAD)7
Skewness4.5777614
Sum129827
Variance634.24505
MonotonicityNot monotonic
2023-02-07T07:56:08.959377image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1963
22.7%
1 606
 
7.0%
12 537
 
6.2%
2 345
 
4.0%
6 340
 
3.9%
3 294
 
3.4%
4 277
 
3.2%
7 265
 
3.1%
8 263
 
3.0%
5 254
 
2.9%
Other values (163) 3488
40.4%
ValueCountFrequency (%)
0 1963
22.7%
1 606
 
7.0%
2 345
 
4.0%
3 294
 
3.4%
4 277
 
3.2%
5 254
 
2.9%
6 340
 
3.9%
7 265
 
3.1%
8 263
 
3.0%
9 240
 
2.8%
ValueCountFrequency (%)
358 1
< 0.1%
347 1
< 0.1%
344 1
< 0.1%
309 1
< 0.1%
308 1
< 0.1%
298 1
< 0.1%
274 1
< 0.1%
273 1
< 0.1%
254 1
< 0.1%
248 2
< 0.1%

credit_limit
Real number (ℝ)

Distinct203
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4521.6379
Minimum50
Maximum30000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-02-07T07:56:09.134232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile1000
Q11600
median3000
Q36500
95-th percentile12000
Maximum30000
Range29950
Interquartile range (IQR)4900

Descriptive statistics

Standard deviation3658.4287
Coefficient of variation (CV)0.80909369
Kurtosis2.7767005
Mean4521.6379
Median Absolute Deviation (MAD)1800
Skewness1.5071901
Sum39030778
Variance13384100
MonotonicityNot monotonic
2023-02-07T07:56:09.306041image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3000 751
 
8.7%
1500 695
 
8.1%
1200 597
 
6.9%
1000 595
 
6.9%
2500 584
 
6.8%
4000 470
 
5.4%
6000 449
 
5.2%
5000 370
 
4.3%
2000 364
 
4.2%
7500 273
 
3.2%
Other values (193) 3484
40.4%
ValueCountFrequency (%)
50 1
 
< 0.1%
150 5
 
0.1%
200 3
 
< 0.1%
300 14
 
0.2%
400 3
 
< 0.1%
450 6
 
0.1%
500 112
1.3%
600 21
 
0.2%
650 1
 
< 0.1%
700 20
 
0.2%
ValueCountFrequency (%)
30000 2
 
< 0.1%
28000 1
 
< 0.1%
25000 1
 
< 0.1%
23000 2
 
< 0.1%
22500 1
 
< 0.1%
22000 1
 
< 0.1%
21500 2
 
< 0.1%
21000 2
 
< 0.1%
20500 1
 
< 0.1%
20000 10
0.1%

payments
Real number (ℝ)

Distinct8632
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1781.8237
Minimum0.049513
Maximum50721.483
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-02-07T07:56:09.493613image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.049513
5-th percentile143.51994
Q1418.75202
median897.28271
Q31951.1421
95-th percentile6142.6959
Maximum50721.483
Range50721.434
Interquartile range (IQR)1532.3901

Descriptive statistics

Standard deviation2895.588
Coefficient of variation (CV)1.6250699
Kurtosis54.549564
Mean1781.8237
Median Absolute Deviation (MAD)592.59303
Skewness5.8694665
Sum15380702
Variance8384429.9
MonotonicityNot monotonic
2023-02-07T07:56:09.677376image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201.802084 1
 
< 0.1%
385.654237 1
 
< 0.1%
904.68554 1
 
< 0.1%
162.949236 1
 
< 0.1%
164.403739 1
 
< 0.1%
1679.00486 1
 
< 0.1%
209.392729 1
 
< 0.1%
1014.549633 1
 
< 0.1%
272.517748 1
 
< 0.1%
32.924384 1
 
< 0.1%
Other values (8622) 8622
99.9%
ValueCountFrequency (%)
0.049513 1
< 0.1%
0.056466 1
< 0.1%
3.500505 1
< 0.1%
4.523555 1
< 0.1%
4.841543 1
< 0.1%
9.533313 1
< 0.1%
12.773144 1
< 0.1%
14.500688 1
< 0.1%
16.385421 1
< 0.1%
18.125527 1
< 0.1%
ValueCountFrequency (%)
50721.48336 1
< 0.1%
46930.59824 1
< 0.1%
40627.59524 1
< 0.1%
39461.9658 1
< 0.1%
39048.59762 1
< 0.1%
36066.75068 1
< 0.1%
35843.62593 1
< 0.1%
34107.07499 1
< 0.1%
33994.72785 1
< 0.1%
33486.31044 1
< 0.1%

min_pay
Real number (ℝ)

Distinct8631
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean864.5275
Minimum0.019163
Maximum76406.208
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-02-07T07:56:09.878529image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.019163
5-th percentile73.396258
Q1169.21645
median312.68409
Q3825.49646
95-th percentile2766.7162
Maximum76406.208
Range76406.188
Interquartile range (IQR)656.28001

Descriptive statistics

Standard deviation2373.0768
Coefficient of variation (CV)2.7449408
Kurtosis283.84406
Mean864.5275
Median Absolute Deviation (MAD)190.50134
Skewness13.619443
Sum7462601.4
Variance5631493.6
MonotonicityNot monotonic
2023-02-07T07:56:10.068629image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
299.351881 2
 
< 0.1%
139.509787 1
 
< 0.1%
277.546713 1
 
< 0.1%
6404.855484 1
 
< 0.1%
616.862544 1
 
< 0.1%
211.984193 1
 
< 0.1%
324.954747 1
 
< 0.1%
1600.26917 1
 
< 0.1%
150.317143 1
 
< 0.1%
216.090433 1
 
< 0.1%
Other values (8621) 8621
99.9%
ValueCountFrequency (%)
0.019163 1
< 0.1%
0.037744 1
< 0.1%
0.05588 1
< 0.1%
0.059481 1
< 0.1%
0.117036 1
< 0.1%
0.261984 1
< 0.1%
0.311953 1
< 0.1%
0.319475 1
< 0.1%
1.113027 1
< 0.1%
1.334075 1
< 0.1%
ValueCountFrequency (%)
76406.20752 1
< 0.1%
61031.6186 1
< 0.1%
56370.04117 1
< 0.1%
50260.75947 1
< 0.1%
43132.72823 1
< 0.1%
42629.55117 1
< 0.1%
38512.12477 1
< 0.1%
31871.36379 1
< 0.1%
30528.4324 1
< 0.1%
29019.80288 1
< 0.1%

prc_full_pay
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1592809
Minimum0
Maximum1
Zeros5587
Zeros (%)64.7%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-02-07T07:56:10.296076image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.166667
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.166667

Descriptive statistics

Standard deviation0.29630102
Coefficient of variation (CV)1.8602419
Kurtosis2.2022385
Mean0.1592809
Median Absolute Deviation (MAD)0
Skewness1.8863927
Sum1374.9128
Variance0.087794293
MonotonicityNot monotonic
2023-02-07T07:56:10.490577image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 5587
64.7%
1 488
 
5.7%
0.083333 426
 
4.9%
0.166667 166
 
1.9%
0.25 156
 
1.8%
0.5 155
 
1.8%
0.090909 153
 
1.8%
0.333333 133
 
1.5%
0.1 94
 
1.1%
0.2 83
 
1.0%
Other values (37) 1191
 
13.8%
ValueCountFrequency (%)
0 5587
64.7%
0.083333 426
 
4.9%
0.090909 153
 
1.8%
0.1 94
 
1.1%
0.111111 61
 
0.7%
0.125 52
 
0.6%
0.142857 54
 
0.6%
0.166667 166
 
1.9%
0.181818 75
 
0.9%
0.2 83
 
1.0%
ValueCountFrequency (%)
1 488
5.7%
0.916667 77
 
0.9%
0.909091 19
 
0.2%
0.9 16
 
0.2%
0.888889 12
 
0.1%
0.875 18
 
0.2%
0.857143 12
 
0.1%
0.833333 63
 
0.7%
0.818182 17
 
0.2%
0.8 33
 
0.4%

tenure
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.534175
Minimum6
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-02-07T07:56:10.645533image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile8
Q112
median12
Q312
95-th percentile12
Maximum12
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3112491
Coefficient of variation (CV)0.11368382
Kurtosis8.1508502
Mean11.534175
Median Absolute Deviation (MAD)0
Skewness-3.0102343
Sum99563
Variance1.7193742
MonotonicityNot monotonic
2023-02-07T07:56:10.777946image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
12 7342
85.1%
11 356
 
4.1%
10 226
 
2.6%
6 184
 
2.1%
8 183
 
2.1%
7 177
 
2.1%
9 164
 
1.9%
ValueCountFrequency (%)
6 184
 
2.1%
7 177
 
2.1%
8 183
 
2.1%
9 164
 
1.9%
10 226
 
2.6%
11 356
 
4.1%
12 7342
85.1%
ValueCountFrequency (%)
12 7342
85.1%
11 356
 
4.1%
10 226
 
2.6%
9 164
 
1.9%
8 183
 
2.1%
7 177
 
2.1%
6 184
 
2.1%

one_payment
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size134.9 KiB
1
4522 
0
4110 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8632
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 4522
52.4%
0 4110
47.6%

Length

2023-02-07T07:56:10.914539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-07T07:56:11.068525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 4522
52.4%
0 4110
47.6%

Most occurring characters

ValueCountFrequency (%)
1 4522
52.4%
0 4110
47.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8632
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4522
52.4%
0 4110
47.6%

Most occurring scripts

ValueCountFrequency (%)
Common 8632
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4522
52.4%
0 4110
47.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8632
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4522
52.4%
0 4110
47.6%

avg_ticket_expenses
Real number (ℝ)

Distinct6330
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.903435
Minimum0.01
Maximum5981.6667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-02-07T07:56:11.247769image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile1
Q11.0458293
median13.676667
Q355.560903
95-th percentile172.4294
Maximum5981.6667
Range5981.6567
Interquartile range (IQR)54.515074

Descriptive statistics

Standard deviation139.58607
Coefficient of variation (CV)2.7971234
Kurtosis442.96243
Mean49.903435
Median Absolute Deviation (MAD)12.676667
Skewness14.883417
Sum430766.45
Variance19484.27
MonotonicityNot monotonic
2023-02-07T07:56:11.442769image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1966
 
22.8%
45.65 17
 
0.2%
50 16
 
0.2%
60 10
 
0.1%
40 10
 
0.1%
100 8
 
0.1%
20 8
 
0.1%
35 7
 
0.1%
25 7
 
0.1%
30 7
 
0.1%
Other values (6320) 6576
76.2%
ValueCountFrequency (%)
0.01 1
 
< 0.1%
0.7 2
 
< 0.1%
0.9363495398 1
 
< 0.1%
0.9458804678 1
 
< 0.1%
0.9745222884 1
 
< 0.1%
0.9985912834 1
 
< 0.1%
0.9994417133 1
 
< 0.1%
0.9998222173 1
 
< 0.1%
0.9998334208 1
 
< 0.1%
1 1966
22.8%
ValueCountFrequency (%)
5981.666667 1
< 0.1%
2600 1
< 0.1%
2523.438 1
< 0.1%
2000 2
< 0.1%
1875 1
< 0.1%
1865.044 1
< 0.1%
1810 1
< 0.1%
1709 1
< 0.1%
1591.5 1
< 0.1%
1500 1
< 0.1%

debit_rate
Real number (ℝ)

SKEWED  UNIQUE 

Distinct8632
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.95522332
Minimum0.00071260404
Maximum95.242586
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-02-07T07:56:11.662708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.00071260404
5-th percentile0.089129372
Q10.38432138
median0.75573772
Q31.1084204
95-th percentile2.57751
Maximum95.242586
Range95.241873
Interquartile range (IQR)0.72409898

Descriptive statistics

Standard deviation1.4140996
Coefficient of variation (CV)1.4803864
Kurtosis2298.4148
Mean0.95522332
Median Absolute Deviation (MAD)0.3625526
Skewness35.884917
Sum8245.4877
Variance1.9996776
MonotonicityNot monotonic
2023-02-07T07:56:11.846157image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2795097625 1
 
< 0.1%
2.309929403 1
 
< 0.1%
1.233056984 1
 
< 0.1%
1.333682746 1
 
< 0.1%
0.3338822657 1
 
< 0.1%
0.498523356 1
 
< 0.1%
4.279276649 1
 
< 0.1%
0.948026992 1
 
< 0.1%
0.06810284004 1
 
< 0.1%
5.012841385 1
 
< 0.1%
Other values (8622) 8622
99.9%
ValueCountFrequency (%)
0.0007126040409 1
< 0.1%
0.0008395366687 1
< 0.1%
0.001332318214 1
< 0.1%
0.00137563274 1
< 0.1%
0.001443141501 1
< 0.1%
0.00159981886 1
< 0.1%
0.001676235847 1
< 0.1%
0.002533629151 1
< 0.1%
0.002878058831 1
< 0.1%
0.003694501127 1
< 0.1%
ValueCountFrequency (%)
95.24258573 1
< 0.1%
17.0099304 1
< 0.1%
14.35301618 1
< 0.1%
13.99995818 1
< 0.1%
13.60114306 1
< 0.1%
12.90542858 1
< 0.1%
12.15774644 1
< 0.1%
11.6374204 1
< 0.1%
10.61397852 1
< 0.1%
10.61189654 1
< 0.1%

credit_limit_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6331
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1247042.8
Minimum0
Maximum1.8389839 × 108
Zeros1966
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-02-07T07:56:12.035990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112196.25
median156128.25
Q3801127.08
95-th percentile5084731.1
Maximum1.8389839 × 108
Range1.8389839 × 108
Interquartile range (IQR)788930.83

Descriptive statistics

Standard deviation5193592.2
Coefficient of variation (CV)4.1647264
Kurtosis391.7112
Mean1247042.8
Median Absolute Deviation (MAD)156128.25
Skewness16.448195
Sum1.0764474 × 1010
Variance2.69734 × 1013
MonotonicityNot monotonic
2023-02-07T07:56:12.243988image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1966
 
22.8%
50000 9
 
0.1%
30000 8
 
0.1%
25000 7
 
0.1%
7608.333333 7
 
0.1%
75000 7
 
0.1%
100000 7
 
0.1%
300000 6
 
0.1%
60000 6
 
0.1%
66666.66667 6
 
0.1%
Other values (6321) 6603
76.5%
ValueCountFrequency (%)
0 1966
22.8%
5 1
 
< 0.1%
6.666666667 1
 
< 0.1%
8.333333333 1
 
< 0.1%
20 1
 
< 0.1%
200 1
 
< 0.1%
513.3333333 1
 
< 0.1%
833.3333333 1
 
< 0.1%
1166.666667 1
 
< 0.1%
1500 1
 
< 0.1%
ValueCountFrequency (%)
183898387.5 1
< 0.1%
133923100 1
< 0.1%
124050920 1
< 0.1%
120122130 1
< 0.1%
116708130 1
< 0.1%
116309466.7 1
< 0.1%
86250000 1
< 0.1%
72606572.5 1
< 0.1%
72576031.67 1
< 0.1%
70262320 1
< 0.1%

Interactions

2023-02-07T07:56:00.080106image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:54:55.591043image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:54:58.540784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:01.782252image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:05.075115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:08.304930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:11.835621image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:14.766244image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:17.904015image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:21.314372image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:24.284802image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:27.350880image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:30.905874image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:33.851990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:37.064104image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:40.176928image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:43.881957image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:47.166182image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:50.185271image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:53.205295image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:56.389750image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:56:00.242421image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:54:55.751353image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:54:58.683630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:01.907380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:05.213948image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:08.463428image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:11.961512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:14.895889image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:18.036745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:21.445569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:24.415824image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:27.491268image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:31.034491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:33.986111image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:37.212378image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:40.324710image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:44.019370image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:47.311960image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:50.316886image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:53.343616image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:56.527931image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:56:00.418872image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:54:55.907487image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:54:58.827456image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:02.055433image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:05.372200image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:08.617429image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:12.119561image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:15.035895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:18.201029image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:21.601450image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:24.581128image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:27.656890image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:31.179748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:34.134074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:37.382395image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:40.491544image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:44.176404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:47.470504image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:50.469441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:53.508909image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:56.675010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:56:00.587247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:54:56.046756image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:54:59.112130image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:02.187251image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:05.527934image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:08.760216image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:12.271566image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:15.201195image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:18.354627image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:21.732847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:24.721720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:27.797171image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:31.312964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:34.270853image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:37.541499image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:40.627688image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:44.332421image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:47.612574image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:50.604007image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:53.651626image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:56.813991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:56:00.736085image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:54:56.173557image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:54:59.248725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:02.320157image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:05.677237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:08.900011image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:12.405244image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:15.341575image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:18.499382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:21.871464image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:24.859523image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:27.938087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:31.447660image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:34.413900image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:37.692254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:40.764795image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:44.481099image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:47.750563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:50.743090image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:53.788792image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:56.970294image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:56:00.911593image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:54:56.317338image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:54:59.401581image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:02.468751image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:05.845596image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:09.054089image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:12.553626image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:15.505895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:18.654443image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:22.050515image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:25.008824image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:28.115920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:31.598720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:34.571686image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:37.850804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:40.934284image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:44.665064image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:47.911532image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:50.895527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:53.964101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:57.144489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:56:01.058053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:54:56.445710image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:54:59.535473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:02.594778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:06.004796image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:09.190290image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:12.685413image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:15.639806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:18.784402image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:22.198660image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:25.138093image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:28.264191image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:31.729596image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:34.704738image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:37.990083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:41.082053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:44.809101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:48.038931image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:51.028753image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:54.108336image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:57.289210image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:56:01.201627image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:54:56.566627image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:54:59.672944image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:02.721607image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:06.160470image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-02-07T07:55:46.373644image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:49.489378image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:52.498955image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:55.586341image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:58.747257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:56:02.783518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:54:57.987839image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:01.211561image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:04.484652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:07.691227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:10.921562image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:14.173454image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:17.243170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:20.732458image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:23.732155image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:26.754774image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:29.905657image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:33.275334image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:36.438756image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:39.608841image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:42.829952image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:46.523646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:49.621845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:52.637061image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:55.741884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:59.478994image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:56:02.956474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:54:58.121187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:01.353556image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:04.626306image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:07.850326image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:11.335995image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:14.328350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:17.400037image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:20.870675image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:23.870003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:26.907280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:30.459073image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:33.420314image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:36.588935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:39.750760image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:43.435921image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:46.674117image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:49.757962image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:52.779134image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:55.905612image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:59.630048image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:56:03.124069image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:54:58.253134image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:01.499013image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:04.766650image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:08.008105image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:11.495413image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:14.489184image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:17.558096image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:21.020804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:24.009753image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:27.059994image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:30.608148image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:33.562058image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:36.737624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:39.891353image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:43.574857image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:46.831262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:49.897520image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:52.920689image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:56.070418image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:59.778530image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:56:03.277970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:54:58.386312image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:01.634794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:04.919403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:08.150205image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:11.646256image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:14.623671image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:17.702390image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:21.169079image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:24.139541image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:27.199679image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:30.745427image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:33.694623image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:36.876735image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:40.023590image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:43.729921image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:46.994462image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:50.031058image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:53.053696image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:56.226214image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-07T07:55:59.911615image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-02-07T07:56:12.438378image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
idbalancebalance_freqpurchasesone_purchasesinstall_purchasescash_advpurchases_freqone_purchases_freqpurchases_install_freqcash_adv_freqcash_adv_trxpurchases_trxcredit_limitpaymentsmin_payprc_full_paytenureavg_ticket_expensesdebit_ratecredit_limit_rateone_payment
id1.000-0.243-0.121-0.118-0.187-0.022-0.047-0.026-0.1810.019-0.021-0.022-0.081-0.375-0.244-0.2050.059-0.169-0.0330.044-0.2010.169
balance-0.2431.0000.512-0.0140.130-0.1040.570-0.1630.103-0.1550.5450.551-0.0650.3780.4190.900-0.5320.056-0.3870.0930.0370.022
balance_freq-0.1210.5121.0000.1280.1160.1170.1280.1950.1410.1510.1670.1660.1890.1000.1610.503-0.2220.226-0.1200.0380.1160.086
purchases-0.118-0.0140.1281.0000.7530.710-0.3870.7940.6940.609-0.396-0.3880.8860.2640.398-0.0080.2330.1290.6980.2810.9510.168
one_purchases-0.1870.1300.1160.7531.0000.208-0.1900.4280.9520.125-0.186-0.1820.5960.3060.3700.0700.0430.0960.4920.2600.7460.144
install_purchases-0.022-0.1040.1170.7100.2081.000-0.3580.7860.1910.922-0.369-0.3600.7840.1270.234-0.0520.2730.1200.4800.1580.6590.099
cash_adv-0.0470.5700.128-0.387-0.190-0.3581.000-0.454-0.194-0.3780.9400.951-0.4100.1650.2660.482-0.280-0.115-0.8290.305-0.3380.035
purchases_freq-0.026-0.1630.1950.7940.4280.786-0.4541.0000.4660.853-0.455-0.4490.9210.1060.164-0.1040.2920.0910.5700.1640.7480.397
one_purchases_freq-0.1810.1030.1410.6940.9520.191-0.1940.4661.0000.120-0.182-0.1800.6110.2830.3240.0510.0560.0840.4440.2250.6950.763
purchases_install_freq0.019-0.1550.1510.6090.1250.922-0.3780.8530.1201.000-0.384-0.3760.7810.0510.112-0.0850.2590.1080.4120.1060.5600.092
cash_adv_freq-0.0210.5450.167-0.396-0.186-0.3690.940-0.455-0.182-0.3841.0000.983-0.4110.0900.2020.456-0.303-0.133-0.8210.242-0.3560.130
cash_adv_trx-0.0220.5510.166-0.388-0.182-0.3600.951-0.449-0.180-0.3760.9831.000-0.4020.0990.2160.472-0.296-0.100-0.8200.248-0.3490.000
purchases_trx-0.081-0.0650.1890.8860.5960.784-0.4100.9210.6110.781-0.411-0.4021.0000.1930.279-0.0260.2480.1640.5670.2050.8420.268
credit_limit-0.3750.3780.1000.2640.3060.1270.1650.1060.2830.0510.0900.0990.1931.0000.4700.2640.0170.1680.0600.1800.4820.229
payments-0.2440.4190.1610.3980.3700.2340.2660.1640.3240.1120.2020.2160.2790.4701.0000.3680.1580.2040.0330.0220.4200.082
min_pay-0.2050.9000.503-0.0080.070-0.0520.482-0.1040.051-0.0850.4560.472-0.0260.2640.3681.000-0.4790.136-0.335-0.0610.0160.034
prc_full_pay0.059-0.532-0.2220.2330.0430.273-0.2800.2920.0560.259-0.303-0.2960.2480.0170.158-0.4791.0000.0130.2830.0080.2170.024
tenure-0.1690.0560.2260.1290.0960.120-0.1150.0910.0840.108-0.133-0.1000.1640.1680.2040.1360.0131.0000.101-0.2080.1600.089
avg_ticket_expenses-0.033-0.387-0.1200.6980.4920.480-0.8290.5700.4440.412-0.821-0.8200.5670.0600.033-0.3350.2830.1011.000-0.0380.6590.084
debit_rate0.0440.0930.0380.2810.2600.1580.3050.1640.2250.1060.2420.2480.2050.1800.022-0.0610.008-0.208-0.0381.0000.2560.000
credit_limit_rate-0.2010.0370.1160.9510.7460.659-0.3380.7480.6950.560-0.356-0.3490.8420.4820.4200.0160.2170.1600.6590.2561.0000.075
one_payment0.1690.0220.0860.1680.1440.0990.0350.3970.7630.0920.1300.0000.2680.2290.0820.0340.0240.0890.0840.0000.0751.000

Missing values

2023-02-07T07:56:03.545667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-07T07:56:04.017623image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idbalancebalance_freqpurchasesone_purchasesinstall_purchasescash_advpurchases_freqone_purchases_freqpurchases_install_freqcash_adv_freqcash_adv_trxpurchases_trxcredit_limitpaymentsmin_payprc_full_paytenureone_paymentavg_ticket_expensesdebit_ratecredit_limit_rate
01000140.9007490.81818295.400.0095.400.0000000.1666670.0000000.0833330.00021000.0201.802084139.5097870.00000012047.7000000.2795101.590000e+04
1100023202.4674160.9090910.000.000.006442.9454830.0000000.0000000.0000000.25407000.04103.0325971072.3402170.2222221201.0000001.2449240.000000e+00
2100032495.1488621.000000773.17773.170.000.0000001.0000001.0000000.0000000.000127500.0622.066742627.2847870.00000012164.4308330.6188579.664625e+05
410005817.7143351.00000016.0016.000.000.0000000.0833330.0833330.0000000.00011200.0678.334763244.7912370.00000012116.0000000.0173323.200000e+03
5100061809.8287511.0000001333.280.001333.280.0000000.6666670.0000000.5833330.00081800.01400.0577702407.2460350.000000120166.6600000.3501903.999840e+05
610007627.2608061.0000007091.016402.63688.380.0000001.0000001.0000001.0000000.0006413500.06354.314328198.0658941.000000121110.7970311.0822041.595477e+07
7100081823.6527431.000000436.200.00436.200.0000001.0000000.0000001.0000000.000122300.0679.065082532.0339900.00000012036.3500000.3601691.672100e+05
8100091014.9264731.000000861.49661.49200.000.0000000.3333330.0833330.2500000.00057000.0688.278568311.9634090.000000121172.2980000.8612821.005072e+06
910010152.2259750.5454551281.601281.600.000.0000000.1666670.1666670.0000000.000311000.01164.770591100.3022620.000000121427.2000001.0130642.349600e+06
10100111293.1249391.000000920.120.00920.120.0000001.0000000.0000001.0000000.000121200.01083.3010072172.6977650.00000012076.6766670.2825921.840240e+05
idbalancebalance_freqpurchasesone_purchasesinstall_purchasescash_advpurchases_freqone_purchases_freqpurchases_install_freqcash_adv_freqcash_adv_trxpurchases_trxcredit_limitpaymentsmin_payprc_full_paytenureone_paymentavg_ticket_expensesdebit_ratecredit_limit_rate
89381917978.8184070.5000000.000.000.001113.1860780.0000000.0000000.0000000.166667701200.01397.77013121.8211940.333333601.0000000.7841600.000000
893919180728.3525481.000000734.40734.400.00239.8910380.3333330.3333330.0000000.166667221000.072.530037110.9507980.000000614.0278105.310043122400.000000
894019181130.8385541.000000591.240.00591.240.0000001.0000000.0000000.8333330.000000061000.0475.52326282.7713201.0000006098.5400001.05901198540.000000
8941191825967.4752700.833333214.550.00214.558555.4093260.8333330.0000000.6666670.6666671359000.0966.202912861.9499060.000000601.0244794.797170321825.000000
89421918340.8297491.000000113.280.00113.280.0000001.0000000.0000000.8333330.000000061000.094.48882886.2831010.2500006018.8800000.62664618880.000000
8943191845.8717120.50000020.9020.900.000.0000000.1666670.1666670.0000000.00000001500.058.64488343.4737170.0000006120.9000000.2046641741.666667
89451918628.4935171.000000291.120.00291.120.0000001.0000000.0000000.8333330.000000061000.0325.59446248.8863650.5000006048.5200000.77739648520.000000
89471918823.3986730.833333144.400.00144.400.0000000.8333330.0000000.6666670.000000051000.081.27077582.4183690.2500006028.8800000.88216024066.666667
89481918913.4575640.8333330.000.000.0036.5587780.0000000.0000000.0000000.16666720500.052.54995955.7556280.250000601.0000000.3375520.000000
894919190372.7080750.6666671093.251093.250.00127.0400080.6666670.6666670.0000000.3333332231200.063.16540488.2889560.000000618.1330978.057147218650.000000